How precious metal and energy resources interact with clean energy stocks? Fresh insight from the novel ARDL technique

How do carbon emissions and eco taxation affect the equity market performance: an empirical evidence from 28 OECD economies

This study examines the nexus between "stock returns," carbon emissions, and "environmental tax" in 28 OECD countries for the period 1994 to 2014. To this end, we employ second-generation econometric techniques that are robust to macroeconomic and financial datasets to eliminate the issues associated with heterogeneity and cross-sectional dependence. The "common correlated effects mean group (CCEMG)" and "augmented mean group (AMG)" estimators are a pioneering attempt to explore the association between the cross sections. These approaches take into account the complexities of real-world data and provide a more accurate understanding of the relationship between the variables. Several studies explored the relationship between CO2 emissions and economic growth. However, the literature lacks studies examining the impact of "environmental tax" implementation on "stock returns," which is a central policy instrument to curb emissions. The results demonstrate a significant and negative relationship between CO2 emissions and "stock market index returns" and a positive and significant relationship between "environmental tax" and "stock market index returns." Furthermore, both these relationships prevail in the world's biggest financial markets and second largest CO2 emitters, the USA. The empirical findings offer various useful implications for investors, policymakers, brokers, corporations, governmental pollution abatement institutions, and other stakeholders who wish to obtain a carbon risk premium.

Source tracing and contagion measurement of carbon emission trading price fluctuation in China from the perspective of major emergencies

Based on monthly economic data spanning from January 2015 to December 2022, we have established an analytical framework to examine the "Russia-Ukraine conflict-financial market pressure and energy market-China carbon emission trading prices." To achieve this objective, we developed indices for financial system pressure, the energy market, and investor sentiment, applying a mediation effects model to validate their transmission mechanisms. Subsequently, the TVP-SV-VAR model was employed to scrutinize the nonlinear impact of the Russia-Ukraine conflict on the valuation of China's carbon emission trading rights. This model integrates time-varying parameters (TVP) and stochastic volatility (SV), utilizing Markov Chain Monte Carlo (MCMC) technology for parameter estimation. Finally, various wavelet analysis techniques, including continuous wavelet transform, cross-wavelet transform, and wavelet coherence spectrum, were applied to decompose time series data into distinct time-frequency scales, facilitating an analysis of the lead-lag relationships within each time series. The research outcomes provide crucial insights for safeguarding the interests of trading organizations, refining the structure of the carbon market, and mitigating systemic risks on a global scale.

Green bond vs. Islamic bond: Which one is more environmentally friendly?

This research investigates the dynamic dependence and causality relationship of the S&P Kensho Clean Energy (CE) and Cleantech (CT) indices with two green bond indices, including the S&P Green Bond Index (GB) and Green Bond Select (GBS) indices, and four Islamic bond indices, including A-, AA-, AAA-, and BBB-graded Sukuk Indices. In the long- and medium-term, the dependence of CE and CT on Sukuk and green bond indices strengthens under normal or bearish market conditions based on quantile cross-spectral (coherency). Overall, among all alternative financing instruments, AA- and A-rated Sukuk indices and GBS indices have higher coherency with CE and CT. Furthermore, the direction of causality in the frequency domain is most commonly observed from Sukuk and bond indices to CT and CE. Once again, AA-rated, A-rated, and GBS have more significant causality effects on CT and CE for nearly all frequencies compared to other indices. These results present vital implications for both policymakers who want to advocate the development of environmentally friendly projects and investors who want to invest their capital in green economic activities in terms of diversifying and hedging their investments.

The asymmetric effect of temperature, exchange rate, metals, and investor sentiments on solar stock price performance in China: evidence from QARDL approach

The study investigates the asymmetric effect of temperature, exchange rate, metals (rare metals and electrical conductors), and investor sentiments on solar stock price performance in China. The novel econometric techniques, i.e., QARDL (quantile autoregressive distributive lag) approach and Granger causality-in-quantiles to analyze the results. In both short- and long-run estimations, the findings suggest that rare metals (cadmium, germanium, indium, and selenium) and electrical conductors (silver, aluminum, and copper) have significant and positive linkage with solar energy stocks at different quantiles based on bullish, bearish, and normal market conditions. On the other hand, negative effects are found for temperature, RMB exchange rate, and investor sentiments in both the short- and long-run. In the short run, the effect of exchange rate varies across different quantiles but it confines to only lower quantiles (bearish market condition) in the longer run. Solar stocks are more prone to investor sentiments under higher quantiles (bullish market conditions). Lastly, we find that temperature is not merely a behavioral anomaly for the solar energy market as it spreads across middle quantiles (normal market conditions) in the longer run. The findings of Granger causality in quantiles further confirm the results of QARDL.

A tested method for assessing and predicting weather-crime associations

Few studies have focused on haze as a weather element and its correlation with crime. In this study, we examined haze as a weather variable to investigate its effects on criminal activity. We used both monthly crime data and weather records to build a regression model that contains a sequential statistical approach to reach the correlation coefficients between the variables. Also, we developed a prediction model to predict crime cases considering three weather factors: temperature, humidity, and haze. We applied this model in two different climate provinces in Saudi Arabia, namely, Riyadh and Makkah. Riyadh is a desert area and observes haze approximately 17 days per month on average, while Makkah is a coastal area observing haze an average of 4 days per month. We found a measurable relationship between each of these three variables and criminal activity. We found that a one-degree increase in temperature was associated with an increase in assault of 0.739, when humidity and haze were held constant. For other independent variables measured against the same crime in Riyadh, a one-degree increase in humidity was associated with a 0.164 increase in assault. An increase in the number of times that a haze phenomenon is observed was associated with an increase of 0.359 in assault cases. Haze had the most effect on theft, drug, and assault crimes in Riyadh compared to the other elements. Temperature and humidity have a significant relationship with crime in Makkah, while haze had no significant influence in that region.

Modeling oil price uncertainty effects on economic growth in Mexico: a sector-level analysis

This paper analyzes the impact of international oil price uncertainty on the different economic sectors (primary, secondary, and tertiary) in Mexico in the period 1993:1-2020:4 through a bivariate structural vector autoregressive (VAR) model with a generalized autoregressive conditional heteroskedasticity (GARCH) in mean to capture the impact of oil volatility on economic growth at the sectoral level of economic activity. The results show that the uncertainty of the international price of oil has a differentiated effect on the different sectors of economic activity in Mexico since it does not influence the primary sector; it negatively impacts the secondary sector, and there is mixed evidence in the tertiary sector. Additionally, evidence is provided that both positive and negative shocks to the international oil price have asymmetric effects at the sectoral level in Mexico. The results highlight the need to implement public policies, at the country level, that help mitigate the effect of uncertainty in the oil market and promote economic stability at the sector level.

Exploring the technical and behavioral dimensions of green supply chain management: a roadmap toward environmental sustainability

Environmental sustainability issues have become an increasing concern for enterprises and organizations due to new tendencies in climate change. Green supply chain management (GSCM) practices are growing worldwide in this context. Based on socio-technical systems and institutional theory, the present study develops a conceptual model highlighting a mediating effect between two distinct categories of GSCM dimensions, i.e., technical practices and behavioral practices, along with the moderating effect of institutional pressure on organizational performance. Data were collected from 260 Pakistani manufacturers, and the structural equation modeling (SEM) approach was employed to analyze the hypotheses. The classification of technical and behavioral GSCM practices and findings of this research contributes to the literature on GSCM. Empirical results reveal that behavioral practices of GSCM (top management support, supplier, and customer involvement) mediate the relationship between technical GSCM practices (eco-design, green manufacturing, and reverse logistics) and organizational performance (economic, environmental, and social). The results also demonstrate that institutional pressure positively moderates the relationship between technical practices and organizational performance. These findings suggest that organizations in developing countries must focus on the behavioral dimensions of GSCM first for the successful implementation of technical dimensions of GSCM to gain effective environmental, economic, and social performance.